Cognitive platform for deriving effort metric for optimizing cognitive treatment

Abstract

Adaptive modification and presentment of user interface elements in a computerized therapeutic treatment regimen. Embodiments of the present disclosure provide for non-linear computational analysis of cData and nData derived from user interactions with a mobile electronic device executing an instance of a computerized therapeutic treatment regimen. The cData and nData may be computed according to one or more artificial neural network or deep learning technique to derive patterns between computerized stimuli or interactions and sensor data. Patterns derived from analysis of the cData and nData may be used to define an effort metric associated with user input patterns in response to the computerized stimuli or interactions being indicative of a measure of user engagement or effort. A computational model or rules engine may be applied to adapt, modify, configure or present one or more graphical user interface elements in a subsequent instance of the computerized therapeutic treatment regimen.

What is claimed is: 
     
       1. A computer-implemented method comprising:
 configuring, with an application server comprising at least one processor, an instance of a cognitive training application for a first end user and an instance of a companion application for the cognitive training application for a second end user; 
 linking, with the application server, the first end user and the second end user in an application database, wherein linking the first end user and the second end user comprises enabling at least one data transfer interface between the cognitive training application and the companion application; 
 presenting, with a first end user computing device communicably engaged with the application server, the instance of the cognitive training application to the first end user, wherein the instance of the cognitive training application comprises one or more computerized stimuli or interactions configured to elicit a specified response from the first end user; 
 receiving, with the application server, a plurality of user activity data comprising a plurality of input sensor data indicative of a plurality of user-generated responses by the first end user to the one or more computerized stimuli or interactions presented during the instance of the cognitive training application; 
 processing, with the application server, the plurality of user activity data according to a machine learning framework, wherein the machine learning framework comprises at least one machine learning model configured to classify one or more stimulus-response patterns from the plurality of user activity data to generate a classified dataset comprising one or more data labels for one or more attributes of the plurality of user activity data; 
 storing, with the application server, the classified dataset in the application database; 
 presenting, with a second end user computing device communicably engaged with the application server, the instance of the companion application for the cognitive training application to the second end user, the instance of the companion application comprising a graphical user interface; 
 configuring or modifying, at the graphical user interface of the companion application, one or more graphical user interface elements for the companion application according to one or more datapoints from the classified dataset; 
 presenting, at the graphical user interface of the companion application, the one or more graphical user interface elements to the second end user, wherein the one or more graphical user interface elements comprise at least one computerized adjustable element configured to provide one or more quantitative metrics for the first end user according to the classified dataset, 
 wherein the at least one computerized adjustable element comprises a graphical indication to the second end user that a degree of effort for the first end user is at or below a target threshold based on an output of the at least one machine learning model, and 
 wherein the at least one machine learning model is configured to analyze the classified dataset to derive one or more patterns from the plurality of user activity data and a temporal relationship of the plurality of input sensor data to determine the degree of effort for the first end user; 
 processing, with the application server, the plurality of user activity data according to the machine learning framework to generate one or more recommendations for improving the degree of effort for the first end user in a subsequent instance of the cognitive training application; 
 presenting, at the graphical user interface of the companion application, the one or more recommendations to the second end user; 
 receiving, via the graphical user interface of the companion application, at least one user-generated input from the second end user in accordance with the one or more recommendations; 
 processing, with the application server, the received at least one user-generated input from the second end user in accordance with the one or more recommendations; and 
 configuring or modifying, with the application server, one or more subsequent computerized stimuli or interactions for the subsequent instance of the cognitive training application to improve the degree of effort for the first end user in response to the processing of the at least one user-generated input from the second end user in accordance with the one or more recommendations. 
 
     
     
       2. The computer-implemented method of  claim 1  wherein the one or more quantitative metrics comprise a quantified number of sessions of the cognitive training application for the first end user for a specified time period. 
     
     
       3. The computer-implemented method of  claim 2  wherein the one or more quantitative metrics comprise a measure of user engagement for the first end user during the quantified number of sessions. 
     
     
       4. The computer-implemented method of  claim 3  wherein the at least one computerized adjustable element is configured to indicate an amount of time the first end user engaged with the instance of the cognitive training application during the specified time period. 
     
     
       5. The computer-implemented method of  claim 3  wherein the one or more graphical user interface elements comprise a graphical indication that the measure of user engagement for the first end user is below a specified threshold for the specified time period. 
     
     
       6. The computer-implemented method of  claim 1  further comprising configuring or modifying, with the application server, the one or more graphical user interface elements for the companion application in response to processing a second or subsequent plurality of user activity data according to the machine learning framework. 
     
     
       7. The computer-implemented method of  claim 1  further comprising providing, with the application server, the one or more quantitative metrics for the first end user to a third end user computing device, wherein the third end user computing device is associated with a third end user comprising a payor user. 
     
     
       8. A computer-implemented system comprising:
 a first end user computing device; 
 a second end user computing device; and 
 an application server communicably engaged with the first end user computing device and the second end user computing device, the application server comprising at least one processor and a non-transitory computer readable medium encoded with one or more processor-executable instructions thereon that, when executed, command the at least one processor to perform one or more operations, the one or more operations comprising: 
 configuring an instance of a cognitive training application for a first end user and an instance of a companion application for the cognitive training application for a second end user; 
 linking the first end user and the second end user in an application database, wherein linking the first end user and the second end user comprises enabling at least one data transfer interface between the cognitive training application and the companion application; 
 presenting the instance of the cognitive training application to the first end user, wherein the instance of the cognitive training application comprises one or more computerized stimuli or interactions configured to elicit a specified response from the first end user; 
 receiving a plurality of user activity data comprising a plurality of input sensor data indicative of a plurality of user-generated responses by the first end user to the one or more computerized stimuli or interactions presented during the instance of the cognitive training application; 
 processing the plurality of user activity data according to a machine learning framework, wherein the machine learning framework comprises at least one machine learning model configured to classify one or more stimulus-response patterns from the plurality of user activity data to generate a classified dataset comprising one or more data labels for one or more attributes of the plurality of user activity data; 
 storing the classified dataset in the application database; 
 presenting the instance of the companion application for the cognitive training application to the second end user, the instance of the companion application comprising a graphical user interface; 
 configuring or modifying, at the graphical user interface of the companion application, one or more graphical user interface elements for the companion application according to one or more datapoints from the classified dataset; 
 presenting, with the instance of the companion application, the one or more graphical user interface elements to the second end user, wherein the one or more graphical user interface elements comprise at least one computerized adjustable element configured to provide one or more quantitative metrics for the first end user according to the classified dataset, wherein the at least one computerized adjustable element comprises a graphical indication to the second end user that a degree of effort for the first end user is at or below a target threshold based on an output of the at least one machine learning model, and 
 wherein the at least one machine learning model is configured to analyze the classified dataset to derive one or more patterns from the plurality of user activity data and a temporal relationship of the plurality of input sensor data to determine the degree of effort for the first end user; 
 processing the plurality of user activity data according to the machine learning framework to generate one or more recommendations for improving the degree of effort for the first end user in a subsequent instance of the cognitive training application; 
 presenting, at the graphical user interface of the companion application, the one or more recommendations to the second end user; 
 receiving, via the graphical user interface of the companion application, at least one user-generated input from the second end user in accordance with the one or more recommendations; 
 processing the received at least one user-generated input from the second end user in accordance with the one or more recommendations; and 
 configuring or modifying one or more subsequent computerized stimuli or interactions for the subsequent instance of the cognitive training application to improve the degree of effort for the first end user in response to the processing of the at least one user-generated input from the second end user in accordance with the one or more recommendations. 
 
     
     
       9. The computer-implemented system of  claim 8  wherein the one or more quantitative metrics comprise a quantified number of sessions of the cognitive training application for the first end user for a specified time period. 
     
     
       10. The computer-implemented system of  claim 9  wherein the one or more quantitative metrics comprise a measure of user engagement for the first end user during the quantified number of sessions. 
     
     
       11. The computer-implemented system of  claim 10  wherein the at least one computerized adjustable element is configured to indicate an amount of time the first end user engaged with the instance of the cognitive training application during the specified time period. 
     
     
       12. The computer-implemented system of  claim 10  wherein the one or more graphical user interface elements comprise a graphical indication that the measure of user engagement for the first end user is below a specified threshold for the specified time period. 
     
     
       13. The computer-implemented system of  claim 8  wherein the one or more operations further comprise operations for configuring or modifying the one or more graphical user interface elements for the companion application in response to processing a second or subsequent plurality of user activity data according to the machine learning framework. 
     
     
       14. The computer-implemented system of  claim 13  wherein the one or more graphical user interface elements for the companion application are configured or modified in real-time in response to processing the second or subsequent plurality of user activity data according to the machine learning framework. 
     
     
       15. The computer-implemented system of  claim 8  wherein the one or more operations further comprise operations for providing the one or more quantitative metrics for the first end user to a third end user computing device, wherein the third end user computing device is associated with a third end user comprising a payor user. 
     
     
       16. A non-transitory computer-readable medium with one or more processor-executable instructions stored thereon that, when executed, command one or more processors to perform one or more operations, the one or more operations comprising:
 configuring an instance of a cognitive training application for a first end user and an instance of a companion application for the cognitive training application for a second end user; 
 linking the first end user and the second end user in an application database, wherein linking the first end user and the second end user comprises enabling at least one data transfer interface between the cognitive training application and the companion application; 
 presenting the instance of the cognitive training application to the first end user, wherein the instance of the cognitive training application comprises one or more computerized stimuli or interactions configured to elicit a specified response from the first end user; 
 receiving a plurality of user activity data comprising a plurality of input sensor data indicative of a plurality of user-generated responses by the first end user to the one or more computerized stimuli or interactions presented during the instance of the cognitive training application; 
 processing the plurality of user activity data according to a machine learning framework, wherein the machine learning framework comprises at least one machine learning model, wherein the machine learning framework is configured to classify one or more stimulus-response patterns from the plurality of user activity data to generate a classified dataset comprising one or more data labels for one or more attributes of the plurality of user activity data; 
 storing the classified dataset in the application database; 
 presenting the instance of the companion application for the cognitive training application to the second end user, the instance of the companion application comprising a graphical user interface; 
 configuring or modifying, at the graphical user interface of the companion application, one or more graphical user interface elements for the companion application according to one or more datapoints from the classified dataset; 
 presenting, with the instance of the companion application, the one or more graphical user interface elements to the second end user, wherein the one or more graphical user interface elements comprise at least one computerized adjustable element configured to provide one or more quantitative metrics for the first end user according to the classified dataset, 
 wherein the at least one computerized adjustable element comprises a graphical indication to the second end user that a degree of effort for the first end user is at or below a target threshold based on an output of the at least one machine learning model, and 
 wherein the at least one machine learning model is configured to analyze the classified dataset to derive one or more patterns from the plurality of user activity data and a temporal relationship of the plurality of input sensor data to determine the degree of effort for the first end user; 
 processing the plurality of user activity data according to the machine learning framework to generate one or more recommendations for improving the degree of effort for the first end user in a subsequent instance of the cognitive training application; 
 presenting, at the graphical user interface of the companion application, the one or more recommendations to the second end user; 
 receiving, via the graphical user interface of the companion application, at least one user-generated input from the second end user in accordance with the one or more recommendations; 
 processing the received at least one user-generated input from the second end user in accordance with the one or more recommendations; and 
 configuring or modifying one or more subsequent computerized stimuli or interactions for the subsequent instance of the cognitive training application to improve the degree of effort for the first end user in response to the processing of the at least one user-generated input from the second end user in accordance with the one or more recommendations.